@ramrod97, I understand it's not ranking data. Thus, calculating the mean rank for each day as in my plot above does not make sense. Rather, you can just count how often each day was selected.
In the code below, I explicitely used intermediate steps (rather than piping it all together with >%>) so that you can follow along more easily.
library("dplyr")
library("tidyr")
library("ggplot2")
Testy <- structure(list(ID = 14:19,
Q5.1 = c("Wednesday", "Monday", "Thursday", "Tuesday",
"Tuesday", "Wednesday"),
Q5.2 = c("Thursday", "Thursday", "Friday", "Thursday",
"Wednesday", "Friday"),
Q5.3 = c(NA, "Friday", NA, NA, "Thursday", NA)),
class = "data.frame", row.names = c(NA, -6L))
Testy_long_format <- pivot_longer(Testy, cols = -ID, values_to = "day", values_drop_na = TRUE)
Testy_long_format$day <- factor(Testy_long_format$day,
levels = c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday"))
Testy_summarized <- count(Testy_long_format, day)
# Alternative to count(): group_by(Testy_long_format, day) %>% summarize(n = n())
ggplot(Testy_summarized, aes(x = day, y = n)) +
geom_col()
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